Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "16" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.269621 | 15.589994 | 14.355232 | 15.282769 | 13.000102 | 15.818319 | 1.515739 | 1.521184 | 0.0790 | 0.0348 | 0.0269 | nan | nan |
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.948752 | 17.022073 | 11.668219 | 12.656695 | 9.131458 | 10.402257 | 1.196046 | 1.279433 | 0.0255 | 0.0249 | 0.0013 | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.044256 | -0.660832 | 13.006209 | 0.898091 | 7.248615 | 2.361484 | 0.784237 | 3.850205 | 0.0321 | 0.5851 | 0.4518 | nan | nan |
| 2460008 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 14.536687 | -0.786442 | 14.238769 | 1.174279 | 6.585728 | 1.937926 | 4.432744 | 2.108433 | 0.0343 | 0.6308 | 0.4941 | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.778180 | -0.693338 | 11.136699 | 1.116891 | 5.859862 | 2.032185 | 1.484705 | 2.927536 | 0.0323 | 0.5941 | 0.4594 | nan | nan |
| 2459999 | digital_ok | 0.00% | 99.83% | 99.83% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3109 | 0.2321 | 0.2193 | nan | nan |
| 2459998 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.189732 | -0.481157 | 9.526580 | 0.895096 | 7.856871 | 2.597585 | 0.721616 | 2.289179 | 0.0309 | 0.6044 | 0.4822 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.072731 | -0.745706 | 10.093652 | 0.901560 | 7.625719 | 1.808856 | 1.674305 | 3.777152 | 0.0331 | 0.6195 | 0.5023 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.174616 | -0.223251 | 12.665316 | 0.993484 | 7.219150 | 2.156950 | 0.462620 | 0.361633 | 0.0318 | 0.6306 | 0.5050 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.381308 | -0.808614 | 11.766373 | 0.697426 | 7.901465 | 2.882777 | 0.302262 | 1.389181 | 0.0356 | 0.6215 | 0.4932 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.887296 | -0.885233 | 10.157821 | 0.956839 | 7.696881 | 1.941122 | 0.210789 | 2.344336 | 0.0318 | 0.6128 | 0.4853 | nan | nan |
| 2459993 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.060718 | -0.671009 | 9.441064 | 0.796616 | 10.066583 | 2.015550 | 0.778634 | 2.749475 | 0.0290 | 0.6168 | 0.4647 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.901976 | -1.104856 | 10.006926 | 0.897450 | 9.081576 | 2.370310 | 0.262071 | 2.428141 | 0.0312 | 0.6170 | 0.4910 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.455883 | -0.813173 | 9.802132 | 0.894502 | 8.996139 | 2.113281 | 0.175375 | 2.347402 | 0.0330 | 0.6169 | 0.4922 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.227324 | -0.940379 | 8.722434 | 1.042046 | 7.937637 | 2.003082 | -0.021454 | 1.687581 | 0.0303 | 0.6157 | 0.4914 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.267509 | -0.968527 | 10.108349 | 0.760986 | 10.707657 | 1.647545 | 0.093406 | 1.927982 | 0.0304 | 0.6096 | 0.4957 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.128066 | -0.728777 | 9.797077 | 0.794521 | 6.327234 | 1.771893 | 0.755795 | 1.629786 | 0.0330 | 0.6224 | 0.5014 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.659079 | -0.873260 | 10.732571 | 0.735956 | 9.289405 | 2.081587 | 5.434396 | 1.894610 | 0.0317 | 0.6444 | 0.5252 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.624792 | -0.738201 | 9.941806 | 0.725028 | 7.158202 | 1.680595 | 1.044360 | 5.354158 | 0.0316 | 0.6204 | 0.5028 | nan | nan |
| 2459984 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.086540 | -0.571643 | 10.304240 | 0.612398 | 9.384578 | 1.290409 | 1.962227 | 1.204743 | 0.0342 | 0.6379 | 0.5214 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.837405 | -0.849476 | 9.877306 | 0.745004 | 9.187044 | 1.539856 | 2.781101 | 1.884019 | 0.0332 | 0.6531 | 0.5317 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.076134 | -0.098187 | 8.382339 | 0.877948 | 4.464968 | 1.238859 | 2.375547 | 0.741837 | 0.0316 | 0.6923 | 0.5659 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.071387 | -0.779054 | 10.533129 | 0.748620 | 10.340191 | 1.837802 | 0.226078 | 2.416912 | 0.0336 | 0.6233 | 0.5034 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.888982 | -0.924682 | 9.461399 | 0.703562 | 8.932414 | 1.539624 | 5.124936 | 1.843725 | 0.0330 | 0.6674 | 0.5471 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.274167 | -0.974578 | 8.768679 | 0.751454 | 8.857285 | 1.700243 | 0.422406 | 2.202666 | 0.0335 | 0.6194 | 0.5104 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.396956 | -0.791062 | 9.523323 | 0.744143 | 9.260345 | 1.489498 | -0.056623 | 2.614082 | 0.0301 | 0.6206 | 0.5048 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.689679 | -0.802756 | 9.349987 | 0.692462 | 9.183711 | 2.051908 | 0.723376 | 2.466724 | 0.0338 | 0.5810 | 0.4701 | nan | nan |
| 2459976 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.611062 | -0.846896 | 9.843137 | 0.736152 | 9.318771 | 2.377564 | 0.686156 | 1.299588 | 0.0309 | 0.6277 | 0.5075 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | nn Temporal Variability | 15.818319 | 12.269621 | 15.589994 | 14.355232 | 15.282769 | 13.000102 | 15.818319 | 1.515739 | 1.521184 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | nn Shape | 17.022073 | 12.948752 | 17.022073 | 11.668219 | 12.656695 | 9.131458 | 10.402257 | 1.196046 | 1.279433 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 13.006209 | 12.044256 | -0.660832 | 13.006209 | 0.898091 | 7.248615 | 2.361484 | 0.784237 | 3.850205 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 14.536687 | -0.786442 | 14.536687 | 1.174279 | 14.238769 | 1.937926 | 6.585728 | 2.108433 | 4.432744 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 11.136699 | 10.778180 | -0.693338 | 11.136699 | 1.116891 | 5.859862 | 2.032185 | 1.484705 | 2.927536 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 9.526580 | 9.189732 | -0.481157 | 9.526580 | 0.895096 | 7.856871 | 2.597585 | 0.721616 | 2.289179 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 10.093652 | 10.072731 | -0.745706 | 10.093652 | 0.901560 | 7.625719 | 1.808856 | 1.674305 | 3.777152 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 12.665316 | 11.174616 | -0.223251 | 12.665316 | 0.993484 | 7.219150 | 2.156950 | 0.462620 | 0.361633 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 11.766373 | 11.381308 | -0.808614 | 11.766373 | 0.697426 | 7.901465 | 2.882777 | 0.302262 | 1.389181 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.887296 | 10.887296 | -0.885233 | 10.157821 | 0.956839 | 7.696881 | 1.941122 | 0.210789 | 2.344336 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 12.060718 | 12.060718 | -0.671009 | 9.441064 | 0.796616 | 10.066583 | 2.015550 | 0.778634 | 2.749475 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 12.901976 | 12.901976 | -1.104856 | 10.006926 | 0.897450 | 9.081576 | 2.370310 | 0.262071 | 2.428141 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.455883 | -0.813173 | 10.455883 | 0.894502 | 9.802132 | 2.113281 | 8.996139 | 2.347402 | 0.175375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.227324 | -0.940379 | 10.227324 | 1.042046 | 8.722434 | 2.003082 | 7.937637 | 1.687581 | -0.021454 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 12.267509 | -0.968527 | 12.267509 | 0.760986 | 10.108349 | 1.647545 | 10.707657 | 1.927982 | 0.093406 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.128066 | 10.128066 | -0.728777 | 9.797077 | 0.794521 | 6.327234 | 1.771893 | 0.755795 | 1.629786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 12.659079 | -0.873260 | 12.659079 | 0.735956 | 10.732571 | 2.081587 | 9.289405 | 1.894610 | 5.434396 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 11.624792 | -0.738201 | 11.624792 | 0.725028 | 9.941806 | 1.680595 | 7.158202 | 5.354158 | 1.044360 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 11.086540 | 11.086540 | -0.571643 | 10.304240 | 0.612398 | 9.384578 | 1.290409 | 1.962227 | 1.204743 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.837405 | 10.837405 | -0.849476 | 9.877306 | 0.745004 | 9.187044 | 1.539856 | 2.781101 | 1.884019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 9.076134 | 9.076134 | -0.098187 | 8.382339 | 0.877948 | 4.464968 | 1.238859 | 2.375547 | 0.741837 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Power | 10.533129 | -0.779054 | 10.071387 | 0.748620 | 10.533129 | 1.837802 | 10.340191 | 2.416912 | 0.226078 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 9.888982 | -0.924682 | 9.888982 | 0.703562 | 9.461399 | 1.539624 | 8.932414 | 1.843725 | 5.124936 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.274167 | 10.274167 | -0.974578 | 8.768679 | 0.751454 | 8.857285 | 1.700243 | 0.422406 | 2.202666 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.396956 | -0.791062 | 10.396956 | 0.744143 | 9.523323 | 1.489498 | 9.260345 | 2.614082 | -0.056623 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.689679 | 10.689679 | -0.802756 | 9.349987 | 0.692462 | 9.183711 | 2.051908 | 0.723376 | 2.466724 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | N01 | digital_ok | ee Shape | 10.611062 | -0.846896 | 10.611062 | 0.736152 | 9.843137 | 2.377564 | 9.318771 | 1.299588 | 0.686156 |